64 citations · 157 across the 11 of their papers we have counts for
11 papers
Genuine Information Needs of Social Scientists Looking for Data
Andrea Papenmeier, Thomas Krämer, Tanja Friedrich +2
Publishing research data is widely expected to increase its reuse and to inspire new research. In the social sciences, data from surveys, interviews, polls, and statistics are prim…
"I don't know anything about laptops!" - User Perception of Digital Product Advisors Adapting to Their Knowledge Levels
Kevin Schott, Andrea Papenmeier, Daniel Hienert +1
Conversational commerce uses digital assistants to support the search process and decision-making in e-commerce. Effective communication in these interactions can be facilitated by…
Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE): Manifesto
Christine Bauer, Li Chen, Nicola Ferro +19
During the workshop, we deeply discussed what CONversational Information ACcess (CONIAC) is and its unique features, proposing a world model abstracting it, and defined the Convers…
What Did I Say Again? Relating User Needs to Search Outcomes in Conversational Commerce
Kevin Schott, Andrea Papenmeier, Daniel Hienert +1
Recent advances in natural language processing and deep learning have accelerated the development of digital assistants. In conversational commerce, these assistants help customers…
Know What Not To Know: Users' Perception of Abstaining Classifiers
Andrea Papenmeier, Daniel Hienert, Yvonne Kammerer +2
Machine learning systems can help humans to make decisions by providing decision suggestions (i.e., a label for a datapoint). However, individual datapoints do not always provide e…
How Accurate Does It Feel? -- Human Perception of Different Types of Classification Mistakes
Andrea Papenmeier, Dagmar Kern, Daniel Hienert +2
Supervised machine learning utilizes large datasets, often with ground truth labels annotated by humans. While some data points are easy to classify, others are hard to classify, w…
UNDR: User-Needs-Driven Ranking of Products in E-Commerce
Andrea Papenmeier, Daniel Hienert, Firas Sabbah +2
Online retailers often offer a vast choice of products to their customers to filter and browse through. The order in which the products are listed depends on the ranking algorithm…
Dataset of Natural Language Queries for E-Commerce
Andrea Papenmeier, Dagmar Kern, Daniel Hienert +3
Shopping online is more and more frequent in our everyday life. For e-commerce search systems, understanding natural language coming through voice assistants, chatbots or from conv…
Starting Conversations with Search Engines -- Interfaces that Elicit Natural Language Queries
Andrea Papenmeier, Dagmar Kern, Daniel Hienert +3
Search systems on the Web rely on user input to generate relevant results. Since early information retrieval systems, users are trained to issue keyword searches and adapt to the l…
'A Modern Up-To-Date Laptop' -- Vagueness in Natural Language Queries for Product Search
Andrea Papenmeier, Alfred Sliwa, Dagmar Kern +3
With the rise of voice assistants and an increase in mobile search usage, natural language has become an important query language. So far, most of the current systems are not able…
How model accuracy and explanation fidelity influence user trust
Andrea Papenmeier, Gwenn Englebienne, Christin Seifert
Machine learning systems have become popular in fields such as marketing, financing, or data mining. While they are highly accurate, complex machine learning systems pose challenge…